Classification of fog situations based on Gaussian mixture model

In this paper, a learning approach is proposed to classify the fog situations into no fog, fog and dense fog three types. Feature vectors designed according to the contrast and details of foggy images are extracted to form the training set. By using the Gaussian Mixture Model to model the probability density of three situations and learning the parameters of the model with the expectation maximization algorithm, the cluster center as well as the model parameters can be well estimated. Experimental results show that the proposed approach performs well in all situations and is feasible and effective in fog situations recognition.